Bibliographic record
Abstract
Abstract This paper draws from the wrongful convictions of women to interrogate the limits of dominant conceptions of wrongful conviction. Most North American innocence projects turn on a conception of demonstrable factual innocence. The paper argues that this focus is problematic as a matter of criminal law principle and presents particular difficulties for women. The paper identifies that family violence forms the primary context for both the conviction of women for violent crimes, and for women's wrongful convictions. Taking two key examples of family violence – child homicide and intimate partner violence – we illustrate that the prevailing focus on demonstrable factual innocence fits awkwardly with identified wrongful convictions in these areas, and argue that this focus may deflect attention from unidentified miscarriages of justice. We suggest that focusing on factual innocence undermines the criminal justice system's proper focus on state responsibilities, including the responsibility to protect women and children from harm, and the asymmetric burden of proof that applies in criminal cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.109 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".